ESE 3060 — Deep Learning Speedrun

This project focuses on a critical challenge in deep learning: improving training efficiency to reduce computational costs and accelerate model convergence across two fundamental domains.

Research Objectives:

The work targets training speed improvements in:

• CIFAR-10 Classification - Optimizing VGG-style networks for faster image recognition training • NanoGPT Language Modeling - Enhancing GPT-style transformer training loops for reduced compute requirements

Methodology:

The approach combines systematic experimentation with rigorous ablation studies:

CIFAR-10 Optimizations:

• Data augmentation strategies to improve sample efficiency
• Advanced weight initialization techniques for faster convergence
• Optimizer selection and hyperparameter tuning
• Network architecture modifications for computational efficiency

NanoGPT Enhancements:

• Algorithmic improvements to training loop efficiency
• System-level optimizations for memory and compute utilization
• Novel activation functions and attention mechanisms
• Distributed training strategies for faster scaling

Experimental Framework:

Each optimization is tested through comprehensive benchmarking:

• Baseline performance measurement for comparison
• Controlled experiments isolating individual improvements
• Ablation studies to understand contribution of each technique
• Detailed logging of training metrics, GPU utilization, and convergence rates

Technical Impact:

The results demonstrate significant improvements in training efficiency without compromising model accuracy. Specific optimizations achieved:

• Reduced training time through improved data pipelines
• Faster convergence via better initialization and optimization strategies
• Lower computational costs through architectural improvements
• Enhanced scalability for larger model training

Research Contribution:

This work provides valuable insights into deep learning optimization, offering practical techniques that can be applied to a wide range of machine learning applications. The systematic approach to experimentation and rigorous evaluation ensures that reported improvements are reliable and reproducible.

The project advances the field of efficient deep learning by demonstrating how thoughtful algorithmic and system-level improvements can substantially reduce the environmental and financial costs of training modern neural networks.